基于化学知识的框架,用于隐私意识的回复合成学习
Guikun Chen1, Xu Zhang1, Xiaolin Hu2
1The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, China.
Nature communications
|September 25, 2025
概括
本研究介绍了基于机器学习的回复合成的隐私保护框架. 基于化学知识的框架 (CKIF) 允许在不共享敏感化学反应数据的情况下进行分布式培训.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 化学信息学 化学信息学
背景情况:
- 化学反应数据对于制药,材料科学和工业化学的进步至关重要.
- 专有反应数据需要严格保密,这对当前的机器学习培训范式构成了挑战.
- 现有的训练回复合成模型的方法涉及从多个来源汇总数据,由于数据传输和潜在暴露,造成重大隐私风险.
研究的目的:
- 开发一种保护隐私的方法来训练基于机器学习的回归合成模型.
- 在不影响专有数据机密性的情况下,在多个组织中实现回复合成模型的分布式训练.
- 引入基于化学知识的框架 (CKIF) 以确保安全有效的模型培训.
主要方法:
- 开发了基于化学知识的框架 (CKIF),用于维护隐私的分布式培训.
- CKIF以代方式汇总模型参数,避免需要共享原始反应数据.
- 利用预测反应物的化学特性来评估模型行为并确定适应性聚合重量.
主要成果:
- CKIF成功地实现了跨组织的回复合成模型的分布式培训.
- 该框架保持了专有化学反应数据的机密性.
- 在各种反应数据集上,CKIF表现出优越的性能,与几种强有力的基线方法相比.
结论:
- 基于化学知识的框架 (CKIF) 为保护隐私的回复合成模型培训提供了安全有效的解决方案.
- 在处理敏感的化学反应数据时,CKIF解决了对保密的关键需求.
- 这种方法有助于在没有数据泄露的情况下在化学研究中开发协作模型.
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